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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mechanical behaviour of additively manufactured metals

Additive manufacturing is reshaping the production of engineering components in diverse industries, such as the automotive, aerospace, defense, and biomedical sectors, by offering unprecedented design flexibility. The non-equilibrium processing conditions of additive manufacturing generate materials with unique microstructures and tailored mechanical properties that are often unattainable through conventional routes. This review focuses on recent advances in additively manufactured metals that demonstrate distinctive mechanical behaviors, including strength-ductility synergy, microstresses and gradient plasticity, fracture and fatigue resistance, and high-temperature creep performance. Here, we examine the mechanisms and micromechanical effects arising from the heterogeneous microstructures fabricated by additive manufacturing, to guide the design of a wide range of high-performance structural materials. Furthermore, we discuss critical research needs and emerging opportunities in process control, alloy design, advanced characterization, high-fidelity computational modeling, and machine learning aimed at achieving exceptional mechanical properties in additively manufactured metals.

Additive Manufacturing↗
DOE OSTI2022

Defects and anomalies in powder bed fusion metal additive manufacturing

Metal additive manufacturing is a disruptive technology that is revolutionizing the manufacturing industry. Despite its unrivaled capability for directly fabricating metal parts with complex geometries, the wide realization of the technology is currently limited by microstructural defects and anomalies, which could significantly degrade the structural integrity and service performance of the product. Accurate detection, characterization, and prediction of these defects and anomalies have an important and immediate impact in manufacturing fully-dense and defect-free builds. As such, this review seeks to elucidate common defects/anomalies and their formation mechanisms in powder bed fusion additive manufacturing processes. They could arise from raw materials, processing conditions, and post-processing. While defects/anomalies in laser welding have been studied extensively, their formation and evolution remain unclear. Additionally, the existence of powder in powder bed fusion techniques may generate new types of defects, e.g., porosity transferring from powder to builds. Practical strategies to mitigate defects are also addressed through fundamental understanding of their formation. Such explorations enable the validation and calibration of models and ease the process qualification without costly trial-and-error experimentation.

42 ENGINEERING↗
DOE OSTI2022

DEFECT DETECTION USING DYNAMIC ANALYSIS FOR ADDITIVE MANUFACTURED METALS

Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.

Deonarain, Gita↗
DOE OSTI2022

Defect and satellite characteristics of additive manufacturing metal powders

Metal additive manufacturing (AM) requires high-quality metal powders to three-dimensionally (3D) print metallic components with complex and customizable geometries. The lack of quantification of AM metal powders creates quality control challenges for 3D printed components, increases the uncertainty of printing reliability and net cost of inspected and certified printed components, and reduces the recyclability of used powders. However, critical characteristics of AM metal powders that are decisive factors for the 3D printing process, such as internal porosity, contamination, and satellite feature, remain ambiguous. In this work, we developed a novel approach to 3D quantify key characteristics of AM metal powders down to individual particles by using high-resolution synchrotron x-ray computed tomography. Empowered by the penetrative capability of high-energy x-ray, internal porosity and contamination within as-atomized metal powders from high-entropy alloys to nickel-based superalloys were evaluated. Additionally, the newly-developed dispersion method enables the homogeneous separation of individual particles, and consequently, results in the implementation of 3D particle shape analysis. To resolve a major challenge of identification and quantification of satellite-feature particles in as-atomized AM metal powders, the satellite features were quantitated by modeling and analyzing the shape parameter of local thickness variance. Furthermore, the 3D analytical methods of particle assessment in this study can be applied to other materials systems like rock, food, and pharmaceutical particles, and provide insights for process optimization across powder metallurgy, concrete, food and pharmaceutical manufacturing, and AM industries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Processing and Archiving Camera Data Effectively for Operando Neutron Measurement of Metal Additive Manufacturing

As the name suggests, The Operando Neutron Measurement of Metal Additive Manufacturing project conducted by ORNL’s Manufacturing Demonstration Facility (MDF) is experimenting with advanced additive metal manufacturing techniques while analyzing the process using the Spallation Neutron Source’s (SNS) beamline. As part of this experiment, the MDF is seeking to employ 2 XIMEA visible light cameras and a single infrared camera to analyze and correct manufacturing in real-time. The MDF requires a solution for capturing the high-resolution data feed from the cameras with compression while preserving enough detail for their software to detect and correct errors in real time. Our solution was to develop a Robot Operating System (ROS) driver to feed the camera data into ROS. From ROS, the feed is compressed and temporarily stored locally to a stripped 4 NVMe SSD RAID array. Post-experiment, the data is transitioned to long-term storage for archival purposes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analysis of Conduction Cooling Strategies for Wire Arc Additive Manufacturing

Metal additive manufacturing (AM) processing consists of numerous parameters which take time to optimize for various geometries. One aspect of the metal AM process that continues to be explored is the control of thermal energy accumulation during component manufacturing due to the melting and solidification of the feedstock. Excessive energy accumulation causes thermal failure of the component while minimal energy accumulation causes lack of fusion with the build plate or previous layer. The ability to simulate the thermal response of an AM component can increase research efficiency by reducing the time to optimize thermal energy accumulation. This paper presents an effective implementation of finite element analysis to determine the thermal response of a wire arc additive manufactured component with various build plate sizes and cooling methods including, integral build plate cooling, oversized build plates with passive cooling, and non-integral build plate cooling. The use of integral build plate cooling channels was shown to decrease the interpass temperature at the conclusion of the build process by 55% and build plate temperature by 96% compared to the conventionally deposited sample with 20 second dwell time. The use of a tall build plate with passive cooling was shown to reduce the interpass temperature by 32% as compared to the conventionally deposited sample with 20 second dwell time. Each cooling strategy evaluated decreased the interpass temperature within a range of 20–55% which enables higher deposition rates and decreased dwell times during depositions. The cooling strategies are designed to be implemented in a hybrid or retrofit AM platform to mitigate concerns of the thermal input from the additive process having detrimental effects on the precision of the machining process. This paper shows that accurate simulations of all strategies can be used to accurately predict the thermal response of the various strategies discussed. These cooling strategies will allow for increased deposition rates with comparable interpass temperature and decreased dwell time, increasing deposition efficiency. This model and these simulations are verified by experimental results. It is concluded that passive strategies, such as the over-sized tall build plate, can be used when liquid coolant in the AM environment could negatively affect the deposition process. Active cooling strategies, such as the integral build plate cooling could be used if low thermal conductivity materials are deposited or higher material deposition rates are desired. This paper discusses the use of active and passive cooling used during AM and shows how a simulation model can be used to make design choices for cooling strategies. The model also enables verification of select critical process parameters such as dwell times for a desired interpass temperature.

Heinrich, Lauren↗
DOE OSTI2023

Uncertainty Quantification of Metal Additive Manufacturing Processing Conditions Through the use of Exascale Computing

Metal additive manufacturing (AM) is a disruptive manufacturing technology that opens the design space for parts outside those possible from traditional manufacturing methods. In order to accelerate industry and R&D needs to certify AM parts, the Exascale Additive Manufacturing project (ExaAM) has developed a suite of exascale-ready computational tools to model the process-to-structure-to-properties (PSP) relationship for additively manufactured metal components. One such tool is an uncertainty quantification (UQ) pipeline to quantify the effect that uncertainty in processing conditions has on local mechanical responses. We present an overview of this pipeline and its required simulation and workflow codes. Using the Oak Ridge National Laboratory’s (ORNL) exascale computer, Frontier, we utilize this pipeline to cross multiple length and time scales to predict the local mechanical response of a location within a complex AM bridge part, AMB2018-01 produced by the National Institute of Standards and Technology (NIST) as part of their 2018 AM-Bench test series. Our results are then compared to experimental mechanical tests of parts from the NIST build to quantify the error in the ExaAM UQ workflow.

Carson, Robert↗
DOE OSTI2026

A Bayesian inferencing framework for ultrasound wave speed measurements in metal additive manufacturing

Process-related changes during metal additive manufacturing introduce microstructural variability in the material properties of printed parts, directly affecting component reliability. Accurate estimation of these property variations with part performance are essential for quality assurance. Ultrasound testing offers a non-destructive means to estimate mechanical properties and detect defects; however, conventional analysis methods often neglect the influence of microstructural variability, limiting their effectiveness. Here, this research presents a Bayesian inference technique for quantifying wave speed uncertainty from ultrasound measurements of metal additive manufactured parts. By integrating prior ultrasound data with a Bayesian model, the proposed approach generates posterior density estimates of wave speed that systematically account for manufacturing-induced variability and uncertainty. The novelty of this research lies in applying a Bayesian framework to analyze experimental ultrasound measurements within the context of metal additive manufacturing variability. The method enhances the accuracy of wave speed estimation by 64%, defect position by 50% and increases confidence associated with wave speed variance by 30% across different porosity levels, thereby providing a robust foundation for improved decision-making and increased reliability in additively manufactured components.

Additive manufacturing↗
DOE OSTI2020

Advanced Data Preparation Module for Metals Additive Manufacturing

The Mission: Provide the LLNL metals additive manufacturing (AM) team with an advanced AM data preparation module that will analyze and optimize build parameters for metal AM parts fabricated in a laser powder bed fusion (LPBF) system in order increase the first pass success ratio.

42 ENGINEERING↗
DOE OSTI2022

Nanoparticle-enabled increase of energy efficiency during laser metal additive manufacturing

The low energy efficiency of the laser metal additive manufacturing (AM) process is a potential sustainability concern for large-scale industrial production. Explicit investigation of the energy efficiency for laser melting requires the direct characterization of melt pool dimension and vapor depression, which is very difficult due to the opaque nature of the molten metal. Here we report the direct observation and quantification of effects of the TiC nanoparticles on the vapor depression and melt pool formation during laser powder bed fusion (LPBF) of Al6061 by in-situ high-speed high-energy x-ray imaging. Based on the quantification results, we calculated the laser melting energy efficiency (defined here as the ratio of the energy needed to melt the material to the energy delivered by the laser beam) with and without TiC nanoparticles during LPBF of Al6061. Here the results show that adding TiC nanoparticles into Al6061 leads to a significant increase of laser melting energy efficiency (114% increase on average, 521% increase under 312 W laser power, 0.4 m/s scan speed). Systematic property measurement, simulation, and x-ray imaging studies enable us, for the first time, to identify that three mechanisms work together to enhance the laser melting energy efficiency: (1) adding TiC nanoparticles increases the absorptivity; (2) adding TiC nanoparticles decreases the thermal conductivity, and (3) adding TiC nanoparticles enables the initiation of vapor depression and multiple reflection at lower laser power (i.e., lowers the laser power threshold for keyholing). The method and mechanisms of using TiC nanoparticles to increase the laser melting energy efficiency during LPBF of Al6061 we reported here may guide the development of feedstock materials for more energy efficient laser metal AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Metal additive manufacturing simulation across length, time, and computing scales

Metal additive manufacturing (AM) offers a unique opportunity for production of advanced materials and complex geometries. However, variability in microstructure and properties challenges conventional approaches to design, process optimization, qualification, and materials selection. Modeling and simulation can improve understanding of AM processing and materials, but also poses major challenges for existing computational methods. Simultaneously, modern scientific computing hardware has become increasingly complex, most notably with the adoption of hybrid architectures such as Graphical Processing Units (GPUs). If appropriately utilized, emerging computational capabilities provide an opportunity to reveal new insight into AM processing and the resulting material structure and properties. In this review we describe the computational AM landscape, identify critical gaps, and highlight opportunities to impact the development and application of AM. First, the requirements and challenges of representative AM problem statements will be defined. Here, these problems range from scientific studies to industrial applications and are designed to capture the breadth of challenges facing the AM community. Next, the current state of AM modeling and simulation is evaluated, broken down by enabling hardware and software, process simulation, microstructure simulation, and property simulation. Each section describes the diversity of simulation approaches and associated trade-offs in physical fidelity and computational expense. Each area is then assessed based on their suitability and readiness for current and developing computational architectures. Lastly, the greatest opportunities for future research and application are highlighted, including gaps in modeling capabilities, opportunities for near-term application, and key scientific challenges.

additive manufacturing↗
DOE OSTI2021

Metal film for additive metal manufacturing

A metal film for use by an additive manufacturing apparatus is described herein. The metal film has a support structure and metal voxels removably attached to the support structure by respective junctions. When a metal voxel in the metal film is impacted by an energy pulse from an energy source of the additive manufacturing apparatus, one or more junctions that attach the metal voxel to the support structure are broken, resulting in the metal voxel detaching from the metal film (at a desired location in a two-dimensional plane).

Robinson, David↗
DOE OSTI2020

Modeling strength and failure variability due to porosity in additively manufactured metals

To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quantification methodology based on pseudo marginal likelihood and embedded variability techniques. We account for both the porosity resolvable in computed tomography scans of the initial material and the sub-threshold distribution of voids through a physically motivated model. Calibration of the model indicates that the sub-threshold population of defects dominates the yield and failure response. Finally, the technique also allows us to quantify the distribution of material parameters connected to microstructural variability created by the manufacturing process, and, thereby, make assessments of material quality and process control.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advanced Instrumentation for Metal Additive Manufacturing

Laser powder bed fusion (LPBF) is the most widely used process for metal additive manufacturing (AM), particularly where complex geometries provide performance advantages unattainable with traditional manufacturing techniques. However, LPBF is highly sensitive to innate variability in both the powder spreading and fusion steps, often leading to defects such as pores that are difficult to detect yet significantly impair component mechanical properties and fatigue life. This thesis presents a range of novel instruments enabling both precise assessment of powder layer characteristics and in-situ thermal metrology of metal AM to advance the quality control of LPBF. First, leveraging a custom X-ray microscope and a radiation-transport model developed through this work, transmission X-ray imaging is used to study spreading of thin metal powder layers. Effective layer depth is directly mapped at a process-relevant size scale, surpassing optical techniques that can only estimate local deposition from layer surface topography. Layer packing density and quality are shown to be influenced by powder flowability and particle size relative to nominal powder layer thickness. Layer quality is additionally connected to the geometry of the spreading implement and its velocity. This technique and its presented findings enable pairing feedstocks with spreading strategies that create layers with consistent packing density and uniformity. Second, a twofold approach is employed to optically interrogate the laser fusion step of LPBF for observing signatures of defect formation. Aperture division multiplexing is conceptualized, providing for simultaneous laser delivery and high-fidelity infrared (IR) process monitoring through a common optic. In-situ microscopy at 50 μm spatial resolution and at mid-wave IR wavelengths is proven readily achievable with the first purpose-built optic of this type. Next, a bespoke imaging spectrometer, along with a temperature-emissivity separation technique, is used to retrieve accurate process temperatures over a 1000 K range. Data from these instruments are correlated to porosity as fine as 4.3 μm in two LPBF test artifacts, as verified using computed tomography (CT), establishing the viability of robust optically-based component qualification.

Penny, Ryan↗
DOE OSTI2026

Metal additively manufactured wavy fin cold-plate architecture for improved thermal-hydraulic performance

Rapid growth in artificial intelligence and data center workloads demands high-performance liquid cooling to manage increasing chip power. This study presents two metal-additive-manufactured cold plates with sinusoidal fins, constant-amplitude wavy fins and linearly variable-amplitude wavy fins and compares them against metal-additive-manufactured straight fins using experiments conducted at 1 kW heat dissipation as well as high-fidelity 3D conjugate computational fluid dynamic simulations. The cold plates were printed in AlSi10Mg material and underwent design using a Python-automated workflow prior to manufacture and testing. The experiments show that wavy fins reduce the normalized thermal resistance by 35 to 45 % at water flow rates from 1 to 4 LPM. At a fixed 20 kPa pressure drop, the variable-waviness design lowered peak surface temperature by 9 °C and thermal resistance by 51 %, while edge-channel maldistribution in the constant wavy fin design limited gains. A thermal resistance breakdown revealed that 55–63 % of the total thermal resistance in wavy designs comes from base heat conduction, 27–33 % from fin heat conduction, and 9–13 % from fin heat convection, indicating the need to address conduction bottlenecks. Parametric sweeps identify a 3 mm fin pitch as optimal, and that horizontal inlet/outlet manifolds further reduce pressure drop by 30–60 % and thermal resistance by 9–16 % relative to vertical inlet-outlet manifolds. The results yield comprehensive guidelines for fin geometry, manifold alignment, material selection and additive-manufacturing constraints to realize high-performance liquid-cooled cold plates for power-dense electronics.

3d printing↗
DOE OSTI2021

Nondiffractive beam shaping for enhanced optothermal control in metal additive manufacturing

High thermal gradients and complex melt pool instabilities involved in powder bed fusion–based metal additive manufacturing using focused Gaussian-shaped beams often lead to high porosity, poor morphological quality, and degraded mechanical performance. We show here that Bessel beams offer unprecedented control over the spatiotemporal evolution of the melt pool in stainless steel (SS 316L) in comparison to Gaussian beams. Notably, the nondiffractive nature of Bessel beams enables greater tolerance for focal plane positioning during 3D printing. We also demonstrate that Bessel beams significantly reduce the propensity for keyhole formation across a broad scan parameter space. High-speed imaging of the melt pool evolution and solidification dynamics reveals a unique mechanism where Bessel beams stabilize the melt pool turbulence and increase the time for melt pool solidification, owing to reduced thermal gradients. Consequently, we observe a distinctively improved combination of high density, reduced surface roughness, and robust tensile properties in 3D-printed test structures.

36 MATERIALS SCIENCE↗